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Bioactive Luteolin Entrapped Chitosan-PLGA Nanoparticles: Formulation Optimization to In-Vivo Preclinical Evaluation

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Abstract

Diabetes mellitus is an incurable metabolic disease characterized by a change in blood glucose level. Luteolin (LT) is a bioactive flavonoid and reported for potential antidiabetic activity. LT loaded chitosan (CH)—polylactic co-glycolic acid (PLGA) nanoparticles (NPs) were prepared by emulsification—evaporation process and further optimized by Box-Behnken design using CH (A), PLGA (B), and polyvinyl alcohol (C) as formulation variables and their effect evaluated on particle size (Y1), drug loading (Y2), and entrapment efficiency (Y3). CH-PLGA-LT-NPs showed a particle size of 273.43 ± 5.24 nm, DL of 15.25 ± 2.43%, and EE of 65.28 ± 1.76%. The zeta potential was found to be positive with spherical surface morphology. CH-PLGA-LT-NPs exhibited sustained drug release (68.23 ± 6.17%) than LT-dispersion (29.66 ± 4.2%). The permeation study and pharmacokinetic study results revealed 3.19 fold as well as 2.63 fold (AUC0-t) enhancement in pure LT. CH-PLGA-LT-NPs also exhibited a significant (P < 0.05) reduction in blood glucose level in streptozotocin-induced diabetic rats than LT-dispersion. CH-PLGA-LT-NPs also showed notable improvement in biochemical parameters as compared to diabetic control and LT-dispersion group. The results concluded that LT-CH-PLGA-NPs is a good alternative to the treatment of diabetes.

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at Jouf University for funding this work through research grant no (DSR-2021-01-0323).

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Correspondence to Ameeduzzafar Zafar or Syed Sarim Imam.

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The study protocol was approved (Approval Number = 04-03-42) by the Institutional Ethical Committee of Jouf University, Aljouf, Sakaka, KSA. All the animal experiments comply with the ARRIVE guidelines and were carried out in accordance with the guidelines 2.

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Zafar, A., Alruwaili, N.K., Imam, S.S. et al. Bioactive Luteolin Entrapped Chitosan-PLGA Nanoparticles: Formulation Optimization to In-Vivo Preclinical Evaluation. J Clust Sci 34, 437–449 (2023). https://doi.org/10.1007/s10876-022-02232-7

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